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共 107108 条记录，以下是251-260 订阅

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Real-Time Violence Recognition in CCTV Video Surveillance Enhanced by AI Deep Technique 6

Real-Time Violence Recognition in CCTV Video Surveillance En...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Appavu, Narenthirakumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798331522667

Despite its many uses, deep learning (DL) is a key tool in the machine learning (ML) community’s toolbox. Numerous techniques have been developed to identify violence and criminal activity on video since many crimes take place in public areas that are not adequately monitored. The significance of automatic violence detection in surveillance video studies has grown. They do, however, have several drawbacks and frequently rely on particular circumstances. This article presents a method for detecting aggression in surveillance films that is based on convolutional neural networks (CNNs). Accuracy is increased by applying both deep learning and machine learning methods as advised. Performance assessments showed how effectively the suggested approach identifies violent content in video. Test results show that the future solution works better than current approaches for classifying violent then criminal material in films. The models that were previously trained InceptionV3, ResNetV2, Inception_ResNetV2, Violence_Net, and the suggested vioNet remained trained on four datasets. The following were the classification outcomes of the endorsement data for each model: VioNet model 99.72%, InceptionResNetV2 90%, ViolenceNet pseudo-97, and InceptionV3 92%. Due to its easier integration of feature maps, the Dense_Net prototypical was selected for our application arrangement instead of the more intricate Inception or InceptionResNet models. Compared to earlier iterations, its sturdy construction provides greater accuracy and performance with fewer filters and settings. © 2025 IEEE.

关键词： Convolutional neural networks

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PDNet: A Novel Approach to Plant Disease Detection using CNN and Keras 6

PDNet: A Novel Approach to Plant Disease Detection using CNN...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Jain, Garima Shukla, Amita Chaudhary, Anamika Sharma, Nidhi Bisht, Sovers Singh Jain, Ankush Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Delhi India Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9798331522667

This research addresses the pressing global demand for food by leveraging cutting-edge deep learning techniques for automating plant disease detection. Focusing on tomato and potato leaf diseases, the study utilized the PlantVillage dataset to compare classical machine learning methods, such as Local Binary Patterns (LBP) combined with classifiers like kNN, SVM, and Extreme learning Machines, against modern Convolutional Neural Network (CNN) architectures. These included MobileNet, DenseNet201, ResNet50, Inception V3, and VGG16/19. A custom-designed CNN model was developed with unique configurations implemented using Keras and TensorFlow. The model was tested for binary, 6-class, and 10-class classifications, achieving remarkable accuracy rates of 99.6%, 98.4%, and 97.2%, respectively. Notably, after 50 training epochs, the model achieved a peak accuracy of 99.78% for plant disease classification. A practical web-based application was also developed, integrating the best-performing model to facilitate real-time leaf disease detection and notify farmers via email. This approach demonstrates a significant advancement in sustainable agriculture by providing an efficient, accurate, and scalable solution for early disease detection, which is critical for improving crop yield and productivity. © 2025 IEEE.

关键词： Convolutional neural networks

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AI Chatbots as Virtual teaching Assistants 27th

AI Chatbots as Virtual Teaching Assistants

引用

27th international conference on Interactive Collaborative learning, ICL 2024

作者： Kasmaee, Ali Shiraee Mahyar, Hamidreza Faculty of Engineering McMaster University HamiltonON Canada

ISBN: (纸本)9783031835193

Recent advancements in artificial intelligence (AI), particularly in natural language processing (NLP), have led to the development of sophisticated chatbots like ChatGPT. These chatbots excel in answering questions and can significantly improve the learning experience by providing immediate feedback and support to students. They enable continuous learning outside traditional classrooms by understanding and responding to questions with human-like proficiency. However, their responses are limited to the information they were trained on. In this study, we employ the Retrieval-Augmented Generation technique to enable these chatbots to access and retrieve relevant information from external knowledge sources (such as course material) to improve the generation capabilities of these models as virtual teaching assistants. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Chatbots

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Efficient Diabetic Retinopathy Detection using CNNs and Transfer learning for Feature Extraction and Classification 6

Efficient Diabetic Retinopathy Detection using CNNs and Tran...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Kumar, V.T. Ram Pavan Maddineni, Tejaswi Satyanarayana, Dune Prabhu, R. Jenin Karthik, K. Raju, Kachapuram Basava Department of Computer Science Kakaraparti Bhavanarayana College Vijayawada India United States Department of CSE Aditya College of Engineering & Technology Surampalem India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India Department of CSE Chaitanya Bharathi Institute of Technology Hyderabad India Department of AI Anurag University Hyderabad India

ISBN: (纸本)9798331522667

Diabetic retinopathy (DR) is a significant cause of adult blindness and a common consequence in diabetes patients, with the potential for irreversible vision loss as the condition advances. Manual diagnosis of DR by ophthalmologists is a time-consuming and labor-intensive procedure. This study presents an automated method for identifying and classifying DR stages using deep learning (DL) and transfer learning techniques. Convolutional Neural Networks (CNN) and pre-trained architectures such as VGG-16, ResNet101, and DenseNet121 are used to categorize DR into five stages: 0 (No DR), 1 (Mild DR), 2 (Moderate DR), 3 (Severe DR), and 4 (Proliferative DR). The algorithm was trained on a large dataset of roughly 4,665 retinal pictures, allowing for automatic identification of DR progression. Accurate classification is provided by the deep learning algorithms by extracting important information from retinal images. Compared to conventional methods, our methodology shows great promise in handling the intricacy and diversity of retinal pictures, providing a more effective and precise way to diagnose diabetic retinopathy. © 2025 IEEE.

关键词： Convolutional neural networks

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Intelligent Hotel Recommendation System Using Dynamic Graph learning 3

Intelligent Hotel Recommendation System Using Dynamic Graph ...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kiruthiga, G. Kamalesh, S. Jegadeesan, S. Manojkumar, S. Kavinesh, K.K. Ruban, A.S. Karpagam College of Engineering Department of Artificial Intelligence And Data Science Coimbatore India Velammal College of Engineering and Technology Department of Information Technology Tamilnadu Madurai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Inherently, the framework of Inn Suggestion presents an entirely hybrid approach to making hotel recommendations. The framework encompasses content-based and collaborative filtering techniques into a single system by analyzing user behaviors, including past bookings and reviews, besides features, which are generally associated with hotels, such as location and price, to get relevant suggestions. At the heart of the framework lies the Adaptive Meta-learning Hybrid algorithm that coordinates several machine learning techniques, like RNN, CNN, KNN, and Node2Vec. This meta-learning layer can recalculate the selection of algorithms dynamically based on performance in targeted subsets of users. The feature affords rapid adaptation to shifts in the user behavior and underlying data distributions, thus significantly enhancing both accuracy and diversity of recommendations over time. Key features include real-time updates, an intuitive user interface, and a robust feedback system. The technologies which architecture is based on including ***, Python, ***, and cloud services, with which scalability and security are the essence of their execution. Introducing these methodologies with AMLH, the Brilliantly Inn Suggestion Framework aims to change the entire experience of booking hotels. Which means a more dynamic personalized experience through booking in respect of each user's needs and overall satisfaction toward travel planning. © 2025 IEEE.

关键词： Collaborative filtering

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Technology Enhanced teaching for an Industry 4.0 Economy - A case of engineering tuition in an ODL university 16

Technology Enhanced Teaching for an Industry 4.0 Economy - A...

引用

16th international conference on Society and Information Technologies, ICSIT 2025

作者： Lugoma, Masikini Mosia, Ngaka Ilunga, Masengo Department of Mining Minerals and Geomatics Engineering University of South Africa Pretoria South Africa Department of Industrial Engineering and Engineering Management University of South Africa Pretoria South Africa Department of Civil and Environmental Engineering and Building Science University of South Africa Pretoria South Africa

ISBN: (纸本)9781950492831

The mission of engineering technology education is to produce technologists who can work with current technology. Therefore, engineering curricula must be a combination of applied engineering theory and hands-on instruction. Courses on mechanical drawing and design of engineering equipment must be core in the instruction of engineering technologists. engineering design is a linear and morphological process that requires a hands-on approach to instruction. It is mission-critical for engineering technology instruction to emphasize more on synthesis as opposed to analysis, that is, course content must include design thinking-related courses rather than scientific analysis and mathematical modeling. This research paper presents a comprehensive example that illustrates how 3IR (Industrial Revolution) skills can be increased in Industrial engineering instruction. The research employs the use of Lego designs, machine learning, and programming languages. The result manifesting is that engineering graduates acquire a capability in engineering equipment production, automation, and design innovation, indispensable for the 4IR economy. © 2025 international conference on Society and Information Technologies. All Rights Reserved.

关键词： engineering education

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Neural Contextual Adaptation (NCA): For Unsupervised learning in Dynamic Environments 3

Neural Contextual Adaptation (NCA): For Unsupervised Learnin...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Praveen Kumar, G.D. Sanjan, S. Suraj, P. Rohan, U.A. Srinivas, R. Thanush, V. Visvesvaraya Technological University P.E.S College of Engineering Department of Computer Science and Engineering Mandya India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

In recent years, artificial intelligence (AI) has made significant strides in various domains, primarily driven by supervised learning techniques requiring vast amounts of labeled data. However, real-world applications, particularly those involving rapidly changing environments, demand models capable of autonomous adaptation without the need for extensive labeled datasets. In this paper, we present a novel framework for unsupervised learning that allows AI models to dynamically adapt in real-time to changing environments. Our approach leverages a combination of self-organizing maps and contrastive predictive coding to enhance model flexibility and adaptability across diverse settings. We introduce an architecture that incorporates temporal data processing and on-the-fly adaptation mechanisms, enabling the model to learn and generalize from evolving data distributions. Through extensive practical assessments and experimentation, we demonstrate our model's ability to outperform existing unsupervised approaches in real-time object detection and decision-making scenarios. The results, which show a significant reduction in model retraining time and increased adaptability to novel environments, suggest that our framework can be applied to various fields including robotics, autonomous vehicles, and dynamic control systems. © 2025 IEEE.

关键词： Self-supervised learning

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TCP SYN Flood Attack Detection Using Logistic Regression and Multi-Agent Reinforcement learning 4

TCP SYN Flood Attack Detection Using Logistic Regression and...

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4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Sanjay, Mummana Arun Raj Kumar, P. Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal Karnataka State Mangaluru575 025 India Department of Computer Science and Engineering National Institute of Technology Calicut Calicut State Kozhikode673 601 India

ISBN: (纸本)9798331530402

In the realm of cybersecurity, Distributed Denial of Service (DDoS) attacks remain a continuous threat, particularly TCP SYN flood attacks due to their stealthiness and potential for disruption. In this paper, we propose a combination of Multi-Agent Reinforcement learning (MARL) with logistic regression for enhancing TCP SYN attack detection, leveraging Actor-Critic as the reinforcement learning algorithm. A novel approach is introduced for hyperparameter optimization using MARL, offering an alternative to traditional techniques such as GridSearchCV and RandomSearchCV. We present a comparative analysis between traditional logistic regression and MARL enhanced approaches, evaluating their performance using metrics such as accuracy, false negatives, and false positives. Results demonstrate that our proposed approach significantly improves detection accuracy and reduces false positives, underscoring its potential in bolstering cybersecurity defenses against sophisticated DDoS threats. © 2025 IEEE.

关键词： Reinforcement learning

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A Systematic Literature Review on the Feasibility of Dialogue learning Based on Generative AI 24

A Systematic Literature Review on the Feasibility of Dialogu...

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2nd international conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Tian, Yusen Tu, Binghao College of Educational Science and Technology Zhejiang University of Technology Zhejiang Hangzhou China

ISBN: (纸本)9798400711237

This study systematically reviews the feasibility of dialogue learning based on generative AI. Research on mixed reality and generative AI in enhancing teaching effectiveness is increasingly in-depth both domestically and internationally. The development and application of mixed reality and generative AI are accelerating, evolving classroom structures and methods. In the context of rapid social, economic, and technological development, students require more personalized and innovative education, which dialogue learning interventions based on generative AI can effectively address. Through a systematic literature review method, 128 relevant studies from the past decade were systematically analyzed to explore the feasibility of dialogue learning in the context of generative AI. The study finds that the application of generative AI in dialogue learning not only enhances learning outcomes but also excels in personalized learning and timely feedback. However, the diversity and operability of technological implementation and application scenarios require further exploration. This research provides references and insights for future studies by related scholars. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Privacy-Preserving Machine learning Models

Privacy-Preserving Machine Learning Models

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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Chodey, Madhuri Devi Gouthami, E. Rao, KakanaboyinaUpendra Ramana, G.V. Shirisha, N. Kumar, Nellore Manoj Department of Electronics and Communication Engineering Karnataka 584101 India Lendi College of Engineering and Technology Department of Science and Humanities Andhra Pradesh Vizianagaram535005 India Anurag Engineering College Department of Computer Science and Engineering Telangana 508206 India Aditya University Department of Mathematics Andhra Pradesh Surampalem533437 India Mlrit Department of Computer Science and Engineering Telangana Hyderabad500043 India Department of Mathematics Thandalam Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331509828

The rapid adoption of machine learning (ML) across several businesses raises serious concerns about data privacy, particularly when sensitive data is involved. By integrating trustworthy techniques in the preprocessing, feature selection, and classification stages, this research provides a comprehensive methodology for developing machine learning models that preserve privacy. Preprocessing uses data anonymisation techniques like k-anonymity and 1-diversity to protect sensitive properties and maintain data utility. Privacy-aware feature selection, or PAFS, is used to identify and retain characteristics that maximise model performance without compromising privacy. In the classification stage, privacy-preserving neural networks handle anonymised or encrypted input while using secure computing frameworks to provide accurate predictions. For privacy-sensitive applications like healthcare and banking, this integrated approach provides a scalable solution, tackling significant challenges in finding a compromise between privacy and usefulness. Experimental results illustrate the utility of the proposed framework and how it could support ethical and safe machine learning in real-world applications. © 2025 IEEE.

关键词： Adversarial machine learning

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